An energy storage cabin optimal charging control method, device, system and storage medium

Abstract

This invention relates to the field of energy storage compartment charging control technology, and discloses an optimal charging control method, device, system, and storage medium for energy storage compartments. The invention constructs a multi-objective optimization model by integrating electricity price data and equipment operation data, iteratively solves for the optimal charging power reference value, and dynamically calculates the load margin based on the total transformer capacity and average load, switching to the transformer with the largest margin to effectively balance the load distribution of each transformer. This avoids the operational risks of overloading some transformers, fully utilizes idle transformer capacity, and improves the utilization rate of grid-side resources. Simultaneously, with the goal of minimizing charging costs and equipment lifespan losses, it achieves dual optimization of charging economy and equipment operating efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy storage compartment charging control technology, specifically to an optimal charging control method, device, system, and storage medium for energy storage compartments. Background Technology

[0002] In containerized energy storage power station (energy storage compartment) applications, hybrid energy storage architectures, combining the advantages of both energy-type and power-type energy storage components, have become a key direction for improving system performance. Energy-type components such as lithium batteries can achieve large-capacity energy storage, while power-type components such as supercapacitors and flywheels can quickly respond to power fluctuations. The synergy between the two can effectively adapt to the large fluctuations in renewable energy output, improve charging stability and grid adaptability, and is of great significance for optimizing the operating efficiency of energy storage systems and ensuring power supply reliability.

[0003] However, existing energy storage modules mostly employ a fixed "peak shaving and valley filling" strategy to achieve coordinated charging and discharging, which means charging and discharging are based on simple electricity price periods. But the above method cannot take into account the real-time load demand of transformers, resulting in some transformers being overloaded while some capacity is idle, thus failing to optimize charging costs. Summary of the Invention

[0004] This invention provides an optimal charging control method, device, system, and storage medium for energy storage compartments, to solve the problem that existing technologies cannot optimize charging costs by adapting to the real-time load requirements of transformers, resulting in some transformers being overloaded while others are idle.

[0005] In a first aspect, the present invention provides an optimal charging control method for an energy storage compartment, the method comprising: Based on electricity price data and operational data from multiple devices, an optimization model is constructed with the goal of minimizing charging costs and device lifespan loss. The optimization model is iteratively solved, and the optimal charging power reference value for future time periods is obtained based on the solution results. Based on the total capacity, average load, and optimal charging power reference value of each transformer, the load margin of each transformer is calculated, and the transformer with the largest load margin is switched according to the preset charging demand. Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the determination result; The optimal charging power reference value is decomposed into low-frequency components and high-frequency components, and the charging rate of the lithium battery and supercapacitor is controlled according to the low-frequency components and the high-frequency components. The priority of each battery cluster is calculated based on its remaining power and health status in the energy storage compartment, and each battery cluster is charged according to the charging current weight corresponding to the priority.

[0006] This invention constructs a multi-objective optimization model by integrating electricity price data and equipment operation data, iteratively solving for the optimal charging power reference value. It then dynamically calculates the load margin based on the total transformer capacity and average load, switching to the transformer with the largest margin to effectively balance the load distribution across transformers. This avoids the operational risks of overloading some transformers while fully utilizing idle transformer capacity, improving the utilization rate of grid resources. Simultaneously, with the goal of minimizing charging costs and equipment lifespan losses, it achieves dual optimization of charging economy and equipment operating efficiency.

[0007] In one optional implementation, the optimization model, based on electricity price data and operational data from multiple devices, aims to minimize charging costs and device lifespan degradation, and includes: Obtain electricity price data for each hour of the next day, the remaining power, health status, temperature of all battery clusters inside the energy storage compartment, and the real-time load and maximum capacity of each transformer; Based on the electricity price data for each time of day in the future, a function for the change of grid electricity price over time is constructed. The lithium battery life loss rate is determined based on the remaining charge, health status, and temperature of each battery cluster. The transformer life loss rate is determined based on the real-time load and maximum capacity of each transformer. The lifespan loss rate of the supercapacitor is determined based on the charging and discharging power and voltage fluctuation of the energy storage chamber. The total lifespan loss function is calculated using the lithium battery lifespan loss rate, the transformer lifespan loss rate, the supercapacitor lifespan loss rate, and the corresponding weighting coefficients. With the optimization objectives of minimizing charging costs and equipment lifespan loss, an optimization model is constructed using the time-varying function of the grid electricity price and the total lifespan loss function.

[0008] This invention comprehensively collects multi-dimensional key data to accurately construct a function of grid electricity price variation over time and a total lifespan loss function, forming an optimized model that balances charging costs and equipment lifespan. It fully considers time-of-use electricity price differences to reduce charging costs and refines the calculation of lifespan loss for each core device to reduce equipment aging losses. Simultaneously, the model construction process closely integrates key data such as real-time transformer load and capacity, and battery cluster status, achieving precise adaptation of charging strategies to grid-side load and the device's own status.

[0009] In one optional implementation, the step of calculating the load margin of each transformer based on its total capacity, average load, and the optimal charging power reference value, and switching to the transformer with the largest load margin according to preset charging demand, includes: Obtain the total capacity and average load of each transformer; Calculate the sum between the average load of the transformer and the optimal charging power reference value; Calculate the difference between the total capacity of the transformer and the sum value to obtain the load margin of the transformer; The load margins of each transformer are sorted according to a preset order. Based on the sorting results, the transformer with the largest load margin is determined and set as the support transformer. When the margin of the target transformer is less than the preset margin threshold, the charging connection of the target transformer is disconnected and the charging connection of the support transformer is switched to continue charging.

[0010] This invention accurately calculates the difference between the total transformer capacity and the average load and optimal charging power reference values ​​to determine the load margin of each transformer. The supporting transformer with the largest margin is then selected through sorting. When the target transformer's margin is insufficient, the charging connection is switched promptly, achieving dynamic load balancing for the transformers. This avoids the operational risks faced by some transformers due to overloading, fully utilizes the capacity resources of idle transformers, and improves the utilization rate of grid-side equipment.

[0011] In one optional implementation, determining whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjusting the charging power of the hybrid energy storage system based on the determination result, includes: Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system; If so, the charging power of the hybrid energy storage system will be limited.

[0012] This invention limits the charging power exceeding the optimal charging power reference value by determining the relationship between the optimal charging power reference value and the maximum charging power threshold of the hybrid energy storage system. This effectively avoids impact damage to the core components of the energy storage system caused by charging power overload, ensuring the stability and safety of the system's charging and discharging process, while also preventing the decrease in charging efficiency due to power exceeding limits.

[0013] In one optional implementation, the step of decomposing the optimal charging power reference value into low-frequency and high-frequency components, and controlling the charging rate of the lithium battery and supercapacitor according to the low-frequency and high-frequency components, includes: The optimal charging power reference value is decomposed into low-frequency and high-frequency components using a filtering algorithm; The low-frequency component is converted into a control command for the lithium battery, and the lithium battery is charged at a constant power according to the control command. The high-frequency components are converted into pulse current commands for the supercapacitor, and the supercapacitor is controlled to absorb current ripple and transient impacts during the charging process according to the pulse current commands.

[0014] This invention uses a filtering algorithm to precisely separate the low-frequency and high-frequency components of the optimal charging power, achieving a collaborative function between the lithium battery and the supercapacitor. The low-frequency component is adapted to the energy storage characteristics of the lithium battery, and the constant power charging mode improves energy storage efficiency; the high-frequency component is handled by the supercapacitor, which efficiently absorbs current ripple and transient impacts through pulsed current commands. This avoids the damage to the lithium battery caused by high-frequency fluctuations, extending its lifespan, while fully leveraging the power response advantages of the supercapacitor to ensure a stable charging process.

[0015] In one optional implementation, the step of calculating the priority of each battery cluster according to its remaining power and health status, and charging each battery cluster according to the charging current weight corresponding to the priority, includes: The remaining power and health status of each battery cluster in the energy storage compartment are normalized. The priority of each battery cluster is obtained by weighted summation of the remaining power and health status of each battery cluster after normalization. The correction coefficient for each battery cluster is calculated based on the priority of the charging current weight, health status, and temperature. The charging current of each battery cluster is determined according to the correction coefficient of each battery cluster, and each battery cluster is charged according to the charging current of each battery cluster.

[0016] This invention normalizes the remaining charge and health status of battery clusters and determines priorities through weighted summation. It then calculates correction coefficients based on health status and temperature to adapt to differentiated charging currents. This avoids the problems of overcharging old and weak batteries and undercharging new batteries caused by a "one-size-fits-all" charging approach. Furthermore, it slows down battery aging and balances the consistency of each battery cluster through dynamic correction based on temperature and health status.

[0017] In an optional implementation, the method further includes: Real-time monitoring of the voltage, current, insulation resistance, and temperature of each battery cluster in the charging circuit; The voltage, the current, the insulation resistance, and the temperature of each battery cluster are compared with their respective thresholds, and abnormal data are determined based on the comparison results. Based on the abnormal data, corresponding processing measures will be implemented.

[0018] This invention identifies anomalies by real-time monitoring of charging circuit voltage, current, insulation resistance, and battery cluster temperature, and accurately comparing these parameters with corresponding thresholds. It then rapidly executes adaptive processing measures for abnormal data, effectively preventing safety risks such as voltage exceeding limits, current overload, insulation failure, and battery overheating, thus avoiding equipment damage or thermal runaway accidents.

[0019] In a second aspect, the present invention provides an optimal charging control device for an energy storage compartment, the device comprising: The module is used to build an optimization model based on electricity price data and operating data of multiple devices, with the optimization goal of minimizing charging costs and device lifespan loss; The solution module is used to iteratively solve the optimization model and obtain the optimal charging power reference value for future time periods based on the solution results. The calculation module is used to calculate the load margin of each transformer based on the total capacity, average load and the optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand. The judgment module is used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the judgment result. The decomposition module is used to decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and the supercapacitor according to the low-frequency components and the high-frequency components. The charging module is used to calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and to charge each battery cluster according to the charging current weight corresponding to the priority.

[0020] Thirdly, the present invention provides an optimal charging control system for an energy storage compartment, the system comprising a control layer and an execution layer connected in sequence; The control layer is used to construct an optimization model based on electricity price data and operating data of multiple devices, with the optimization objective of minimizing charging costs and device lifespan loss; and to iteratively solve the optimization model to obtain the optimal charging power reference value for future periods based on the solution results. The execution layer is used to calculate the load margin of each transformer based on the total capacity, average load and the optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand. The execution layer is also used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the determination result. The execution layer is also used to decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and the supercapacitor according to the low-frequency components and the high-frequency components. The execution layer is also used to calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and to charge each battery cluster according to the charging current weight corresponding to the priority.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the energy storage compartment optimal charging control method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart of the first method for optimal charging control of an energy storage compartment according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the second process of the optimal charging control method for the energy storage compartment according to an embodiment of the present invention; Figure 3 This is a structural block diagram of the optimal charging control device for the energy storage compartment according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] This invention provides an optimal charging control method for energy storage compartments. By integrating electricity price data and equipment operation data to construct a multi-objective optimization model, the optimal charging power reference value is obtained through iterative solutions. The method dynamically calculates the load margin based on the total transformer capacity and average load, and switches to the transformer with the largest margin, effectively balancing the load distribution of each transformer. This avoids the operational risks of overloading some transformers while fully utilizing idle transformer capacity, improving the utilization rate of grid-side resources. Simultaneously, it aims to minimize charging costs and equipment lifespan losses, achieving a dual optimization of charging economy and equipment operating efficiency.

[0028] According to an embodiment of the present invention, an embodiment of an optimal charging control method for an energy storage compartment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides an optimal charging control method for an energy storage compartment. Figure 1 This is a flowchart of the optimal charging control method for the energy storage compartment according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Based on electricity price data and operating data of multiple devices, an optimization model is constructed with the goal of minimizing charging costs and device lifespan loss.

[0030] It should be noted that the electricity price data refers to the electricity price information divided into different time periods within the next day.

[0031] Operational data refers to the status data of various devices related to the charging of energy storage systems.

[0032] An optimization model is a mathematical model constructed to solve for the optimal charging strategy, with the goal of minimizing charging costs and equipment lifespan loss, and incorporating various constraints.

[0033] In this embodiment of the invention, the following data are obtained: time-of-use electricity price data for each day, remaining charge (SOC), state of health (SOH), and temperature of all battery clusters in the energy storage compartment, as well as real-time load, maximum capacity of each transformer, charging and discharging power of the supercapacitor, voltage fluctuations, and other equipment operation data. First, the time-of-use electricity price function, lithium battery life loss rate, transformer life loss rate, and supercapacitor life loss rate are constructed respectively. Then, the total life loss function is calculated by combining the weight coefficients corresponding to the life loss of each device. Finally, with minimizing charging cost and device life loss as the optimization objective, the above-mentioned time-of-use electricity price function and total life loss function are integrated to construct an optimization model under multiple constraints.

[0034] Step S102: Iteratively solve the optimization model and obtain the optimal charging power reference value for future time periods based on the solution results.

[0035] It should be noted that iterative solution refers to a solution method that gradually approaches the optimal solution of the optimization model by continuously updating the calculation parameters and repeating the calculation process through the algorithm.

[0036] The optimal charging power reference value refers to the ideal charging power standard that adapts to the system's operating state in various future time periods, obtained based on the optimization model.

[0037] In this embodiment of the invention, an optimization algorithm is selected, and reasonable algorithm parameters such as the number of iterations and inertia weights are set. The time-varying function of grid electricity price, the total lifetime loss function, and various constraints are substituted into the model for iterative calculation. During the process, it is verified in real time whether the results of each iteration meet the constraints such as the maximum charging power of the hybrid energy storage system, the total available energy storage capacity, and the battery safe operation threshold. If there is a conflict, the iteration direction is dynamically adjusted until convergence to obtain the optimal solution. This optimal solution is the optimal charging power reference value for each time period in the future. .

[0038] Step S103: Calculate the load margin of each transformer based on the total capacity, average load and optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand.

[0039] It should be noted that total capacity refers to the transformer's rated maximum carrying capacity.

[0040] Average load refers to the average operating power of a transformer over a near-preset time period.

[0041] Load margin refers to the remaining carrying capacity of a transformer after subtracting the sum of the current average load and the optimal charging power reference value from its total capacity.

[0042] Preset charging requirements refer to the pre-defined transformer selection rules.

[0043] In this embodiment of the invention, the real-time load margin of each transformer is calculated one by one using the load margin calculation formula. Then, according to the rules in the preset charging requirements, the load margin of all transformers is sorted and filtered to determine the transformer with the largest load margin. If the load margin of the currently connected target transformer is less than the preset threshold, the charging circuit connection with the target transformer is disconnected, and the transformer with the largest load margin is switched to establish a stable charging connection.

[0044] Step S104: Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the determination result.

[0045] It should be noted that the maximum charging power threshold of a hybrid energy storage system refers to the upper limit of the maximum charging power that the hybrid energy storage system can withstand under the premise of safe and stable operation.

[0046] Hybrid energy storage systems refer to energy storage systems that include lithium batteries, supercapacitors, and other components.

[0047] Charging power refers to the power value of a hybrid energy storage system when it performs a charging action after adjustment and assessment.

[0048] In this embodiment of the invention, the optimal charging power reference value is compared with the preset maximum charging power threshold of the hybrid energy storage system. If the optimal charging power reference value is greater than the maximum threshold, the charging power of the hybrid energy storage system is limited according to the maximum charging power threshold, and the charging power is adjusted to the maximum threshold. If the optimal charging power reference value is not greater than the maximum threshold, the optimal charging power reference value is maintained as the actual charging power, while ensuring that the adjusted charging power is not lower than the minimum charging power threshold of the system.

[0049] Step S105: Decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and supercapacitor according to the low-frequency components and high-frequency components.

[0050] It should be noted that the low-frequency component refers to the stable part of the charging power with low fluctuation frequency.

[0051] High-frequency components refer to the parts of charging power that fluctuate frequently and change instantaneously.

[0052] Charging rate refers to the speed at which an energy storage device receives electrical energy per unit of time.

[0053] In this embodiment of the invention, a filtering algorithm is used to decompose the optimal charging power reference value into a stable low-frequency component and a fluctuating high-frequency component. The low-frequency component is adapted to the energy storage characteristics of lithium batteries, and the high-frequency component matches the power buffering characteristics of supercapacitors. Then, the low-frequency component is converted into a constant power charging control command. The constant power charging control command controls the lithium battery to charge at a stable rate to efficiently store energy. At the same time, the high-frequency component is converted into a pulse current command. The pulse current command regulates the charging and discharging rate of the supercapacitor, enabling it to quickly absorb the current ripple and transient impacts during the charging process.

[0054] Step S106: Calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and charge each battery cluster according to the charging current weight corresponding to the priority.

[0055] It should be noted that the remaining capacity (SOC) refers to the proportion of the battery cluster's current remaining electrical energy to its rated capacity.

[0056] State of Health (SOH) refers to the ratio of the current performance of a battery cluster to its initial performance.

[0057] Priority refers to the charging priority ranking based on a comprehensive evaluation of the battery cluster's SOC, SOH, and temperature.

[0058] Charging current weight refers to the proportion of the total charging current allocated to each battery cluster based on its priority.

[0059] In this embodiment of the invention, the remaining charge (SOC) and state of health (SOH) of each battery cluster in the energy storage compartment are normalized to eliminate dimensional differences. Then, the normalized SOC and SOH are weighted and summed according to preset weighting coefficients to obtain the comprehensive priority of each battery cluster. The higher the priority, the greater the corresponding charging current weight. At the same time, the charging current weight is dynamically adjusted in combination with the real-time temperature of each battery cluster to avoid battery clusters with excessive temperature or poor health being subjected to excessive charging current. Finally, the corresponding charging current is allocated according to the adjusted charging current weight to perform differentiated charging for each battery cluster.

[0060] This embodiment provides an optimal charging control method for an energy storage compartment. Figure 2 This is a flowchart of the optimal charging control method for the energy storage compartment according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Based on electricity price data and operating data of multiple devices, an optimization model is constructed with the goal of minimizing charging costs and device lifespan loss.

[0061] Specifically, step S201 includes: Step S2011: Obtain the electricity price data for each time period in the next day, the remaining power, health status, temperature of all battery clusters inside the energy storage compartment, and the real-time load and maximum capacity of each transformer.

[0062] It should be noted that real-time load refers to the actual electrical load power currently being carried by the transformer.

[0063] Maximum capacity refers to the maximum rated power that a transformer can carry for long-term safe operation.

[0064] In this embodiment of the invention, global data acquisition and prediction involves obtaining time-of-use electricity price data for the next 24 hours. And photovoltaic power generation curve .

[0065] Get the current status of all battery clusters in the energy storage compartment: remaining power. Health status ,temperature .

[0066] Obtain the real-time load of each distribution transformer. and maximum capacity .

[0067] Step S2012: Based on the electricity price data for each time period within the next day, construct a function for the change of grid electricity price over time.

[0068] It should be noted that the function of electricity price change over time refers to a mathematical expression with time as the input variable and electricity price as the output variable.

[0069] In this embodiment of the invention, based on the time-of-use electricity price data divided into 15-minute time granularities for the next day, the function of grid electricity price change over time is constructed using a piecewise constant function, and the calculation expression is as follows:

[0070] In the formula, express t The electricity price on the grid at any given time; t This represents a continuous-time variable with a value range of [0, 24). k This represents the time period number, with values ​​from 0, 1, 2, ..., 95, corresponding to 96 15-minute time periods within the next 24 hours; Indicates the first k The start time of each time period; Indicates the first k The time-of-use electricity price standard corresponding to each time period is determined by the peak, flat, and valley electricity price data obtained from the power grid dispatch interface.

[0071] Step S2013: Determine the lithium battery life loss rate based on the remaining charge, health status, and temperature of each battery cluster.

[0072] It should be noted that the lithium battery life loss rate refers to the proportion of performance degradation of a lithium battery per unit time caused by factors such as charging and discharging operation and ambient temperature.

[0073] In this embodiment of the invention, a loss model incorporating multiple factors such as SOC, SOH, and temperature is used, and the calculation expression is as follows:

[0074] In the formula, Indicates the first i Individual battery clusters t The lifespan and degradation rate of lithium batteries are constantly monitored. This indicates the baseline lifespan loss rate of the lithium battery. Indicates the first i SOC impact coefficient of individual battery clusters; Indicates the first i SOH decay coefficient of individual battery clusters; Indicates the first i Temperature acceleration coefficient of each battery cluster.

[0075] Step S2014: Determine the transformer life loss rate based on the real-time load and maximum capacity of each transformer.

[0076] It should be noted that the transformer life loss rate refers to the proportion of insulation performance degradation of a transformer per unit time due to factors such as thermal aging and load fluctuations.

[0077] In this embodiment of the invention, a loss model is constructed based on the transformer thermal aging theory, and the calculation expression is as follows:

[0078] In the formula, No. j The life loss rate of the transformer at time t; The transformer reference life loss rate is an inherent parameter of the equipment and is calibrated by the manufacturer based on the rated life data. Indicates the first j The load factor influence coefficient of a transformer reflects the accelerating effect of load factor on thermal aging, and its expression is: ; Indicates the first j A transformer in t Real-time load at any given moment; Indicates the first j The maximum capacity of the transformer.

[0079] Step S2015: Determine the supercapacitor life loss rate based on the charging and discharging power and voltage fluctuations of the energy storage compartment.

[0080] It should be noted that the supercapacitor lifespan attenuation rate refers to the proportion of performance degradation of a supercapacitor per unit time caused by factors such as charging and discharging power surges and voltage fluctuations.

[0081] In this embodiment of the invention, real-time charging and discharging power and voltage fluctuation data of the energy storage chamber are used to analyze the impact of power surges and voltage fluctuations on the electrode materials and electrolyte of the supercapacitor. Power surge coefficients and voltage fluctuation coefficients are introduced to quantify the effects of these two factors on lifespan loss. Simultaneously, the lifespan loss rate of the supercapacitor is determined by combining the rated parameters of the supercapacitor. A loss model is constructed based on the electrochemical reaction and material aging characteristics of the supercapacitor, and the calculation expression is as follows:

[0082] In the formula, Indicates the first m A supercapacitor in t Lifetime at any given moment; This represents the reference life loss rate of the supercapacitor, which is an inherent parameter of the equipment and is calibrated by the manufacturer based on the rated cycle life data. Indicates the first m The power surge coefficient of a supercapacitor; Indicates the first m Real-time charging and discharging power of a supercapacitor; Indicates the first m Voltage fluctuation coefficient of a supercapacitor; Indicates the first m A supercapacitor in t The voltage fluctuation amplitude at any given moment.

[0083] Step S2016: Calculate the total lifespan loss function using the lithium battery lifespan loss rate, transformer lifespan loss rate, supercapacitor lifespan loss rate, and corresponding weighting coefficients.

[0084] It should be noted that the total lifetime loss function refers to a mathematical function that integrates the lifetime loss data of core equipment within the system, reflecting the overall lifetime loss of the energy storage system per unit time.

[0085] In this embodiment of the invention, a weighted summation model is used to construct the total lifetime loss function, and the calculation expression is as follows:

[0086] In the formula, express t The lifespan loss of each device at any given moment is a function of time as a function of time (i.e., the total lifespan loss function). Weighting coefficients representing the lifespan loss of lithium batteries; Weighting coefficients representing transformer lifespan losses; Weighting coefficients representing the lifespan loss of supercapacitors; express t The average lifespan loss rate of all lithium batteries in the energy storage compartment at any given time. express t The average lifespan loss rate of all transformers at any given time; express t The average lifespan loss rate of all supercapacitors at any given time.

[0087] Step S2017: With minimizing charging costs and equipment lifespan loss as the optimization objectives, an optimization model is constructed using the grid electricity price change function over time and the total lifespan loss function.

[0088] In this embodiment of the invention, with the optimization objective of minimizing charging costs and device lifespan loss, the calculation formula of the constructed optimization model is as follows:

[0089] In the formula, Indicates the weighting of electricity prices; Indicates the lifespan loss penalty factor; A function representing the change of grid electricity price over time; This indicates the time-of-use electricity price data for the next 24 hours; This is a function representing the change in the lifespan loss of each device over time.

[0090] Step S202: Iteratively solve the optimization model and obtain the optimal charging power reference value for future time periods based on the solution results.

[0091] Please see details Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0092] Step S203: Calculate the load margin of each transformer based on the total capacity, average load and optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand.

[0093] In some optional implementations, step S203 above includes: Step S2031: Obtain the total capacity and average load of each transformer.

[0094] In this embodiment of the invention, the operating load data of each transformer is collected in real time by load sensors, the average load of each transformer is calculated according to a preset 10-minute time window, and the total capacity of each transformer is extracted.

[0095] Step S2032: Calculate the sum between the average load of the transformer and the optimal charging power reference value.

[0096] In this embodiment of the invention, the formula for adding the average load of the transformer and the optimal charging power reference value is as follows:

[0097] In the formula, Indicates the first j Average load of each transformer; This indicates the optimal charging power reference value.

[0098] Step S2033: Calculate the difference between the total capacity of the transformer and the sum value to obtain the load margin of the transformer.

[0099] In this embodiment of the invention, the formula for calculating the load margin of the transformer is as follows:

[0100] In the formula, Indicates the first j Total capacity of the transformers; Indicates the first j Average load of each transformer; This indicates the optimal charging power reference value.

[0101] Step S2034: Sort the load margins of each transformer according to a preset order, determine the transformer with the largest load margin based on the sorting results, and set the transformer with the largest load margin as the support transformer.

[0102] It should be noted that the sorting result refers to the ranking sequence of each transformer after it has been arranged in a preset order.

[0103] Support transformers refer to the transformers that, after sorting and screening, have the largest load margin and meet the conditions for safe operation.

[0104] In this embodiment of the invention, the load margin data is sorted in descending order according to a preset load margin. The load margin of all transformers is sorted, and during the sorting process, it is simultaneously verified whether the real-time load rate of each transformer is lower than a preset safety threshold. Abnormal transformers with load rates exceeding the threshold are removed, and the transformer with the largest load margin at the top of the remaining valid transformers is selected and set as the support transformer.

[0105] Step S2035: When the margin of the target transformer is less than the preset margin threshold, disconnect the charging connection of the target transformer and switch to the charging connection of the support transformer for continuous charging.

[0106] It should be noted that the target transformer refers to the transformer that is currently establishing a charging connection with the hybrid energy storage system.

[0107] The preset margin threshold refers to the pre-set critical value of the load margin.

[0108] In this embodiment of the invention, if the target transformer margin is insufficient, i.e., less than a preset margin threshold, then a search is performed. The largest support transformer, control switch matrix, disconnects the original connection and closes the connection with the support transformer to ensure that the charging process takes place on a line with sufficient capacity.

[0109] Step S204: Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the determination result.

[0110] In some optional implementations, step S204 above includes: Step S2041: Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system.

[0111] In this embodiment of the invention, the optimal charging power reference value is verified in real time, the current operating status of the hybrid energy storage system is collected, and it is determined whether the optimal charging power reference value exceeds the maximum charging power threshold of the hybrid energy storage system.

[0112] Step S2042, if yes, then the charging power of the hybrid energy storage system is limited.

[0113] It should be noted that limiting processing refers to the control signal used to adjust the charging power to a safe threshold.

[0114] In this embodiment of the invention, if the optimal charging power reference value exceeds the maximum charging power threshold of the hybrid energy storage system, the charging power of the hybrid energy storage system is first limited to avoid equipment overload.

[0115] Step S205: Decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and supercapacitor according to the low-frequency components and high-frequency components.

[0116] Specifically, step S205 includes: Step S2051: The optimal charging power reference value is decomposed into low-frequency components and high-frequency components using a filtering algorithm.

[0117] It should be noted that the filtering algorithm refers to a combination of low-pass and high-pass filtering algorithms.

[0118] In this embodiment of the invention, a combined algorithm of low-pass filtering and high-pass filtering is used to optimize the charging power reference value. Decomposed into low-frequency components and high frequency components .

[0119] Step S2052: Convert the low-frequency component into a control command for the lithium battery, and charge the lithium battery at constant power according to the control command.

[0120] It should be noted that constant power charging refers to a charging mode for lithium batteries.

[0121] In this embodiment of the invention, the low-frequency component The control commands are converted into those for the lithium battery and sent to the lithium battery pack via the BMS (Battery Management System) for constant power charging.

[0122] Step S2053: Convert the high-frequency components into pulse current commands for the supercapacitor, and control the supercapacitor to absorb current ripple and transient impacts during the charging process according to the pulse current commands.

[0123] It should be noted that pulse current command refers to a standardized control command that includes parameters such as pulse peak value, duty cycle, and response time.

[0124] Current ripple refers to the high-frequency pulsating component superimposed on the charging current.

[0125] Transient surge refers to the instantaneous power surge caused by sudden load changes, equipment switching, etc. during the charging process.

[0126] In this embodiment of the invention, the high-frequency component Converting pulsed current commands to supercapacitors or converting high-frequency components The speed control command is converted into a flywheel speed control command and sent to the supercapacitor group / flywheel group to absorb current ripple and transient shocks.

[0127] Step S206: Calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and charge each battery cluster according to the charging current weight corresponding to the priority.

[0128] Specifically, step S206 includes: Step S2061: Normalize the remaining power and health status of each battery cluster in the energy storage compartment.

[0129] It should be noted that normalization refers to the method of converting data with different dimensions and different value ranges into a unified standard interval through specific mathematical transformations.

[0130] In this embodiment of the invention, the real-time remaining power (SOC) and state of health (SOH) raw data of each battery cluster in the energy storage compartment are converted to the [0,1] interval by a linear normalization method, taking into account that the two types of data have different dimensions and different value ranges.

[0131] Step S2062: The remaining power and health status of each battery cluster after normalization are weighted and summed to obtain the priority of each battery cluster.

[0132] It should be noted that weighted summation refers to a multi-indicator fusion calculation method.

[0133] In this embodiment of the invention, the combined coefficient of SOC and SOH is obtained by weighted summation ( The weighting coefficients for SOC. The weighting coefficient for SOH. ).

[0134] The formula for calculating the priority of each battery cluster is as follows:

[0135] In the formula, Indicates the first i The overall priority coefficient of each battery cluster; Indicates the first i The weighting coefficient of the SOC of each battery cluster; Indicates the first i The weighting coefficients of SOH for each battery cluster; Indicates the first i The overall SOC coefficient of each battery cluster; Indicates the first i The overall SOH coefficient of each battery cluster.

[0136] Step S2063: Calculate the correction coefficient for each battery cluster based on the priority of the charging current weight, health status, and temperature.

[0137] It should be noted that the correction factor refers to the adjustment factor obtained by combining the charging current weight, health status, and temperature.

[0138] In this embodiment of the invention, the comprehensive coefficient is normalized to obtain the charging current weight of each battery cluster (the sum of all weights is 1). The formula for calculating the charging current weight is as follows:

[0139] In the formula, This represents the charging current weight corresponding to the i-th battery cluster; Indicates the first iPriority coefficient of each battery cluster; Indicates all n The sum of the overall priority coefficients of each battery cluster.

[0140] For the lithium battery pack, calculate the correction factor for each battery cluster:

[0141] In the formula, Indicates the first i Correction coefficients for each battery cluster; Indicates the first i SOC of each battery cluster; Indicates the first i SOH of each battery cluster; Indicates the first i The temperature of each battery cluster.

[0142] if If the difference is large, then It tends towards an equilibrium value; if low or High, then .

[0143] Step S2064: Determine the charging current of each battery cluster according to the correction coefficient of each battery cluster, and charge each battery cluster according to the charging current of each battery cluster.

[0144] It should be noted that the charging current refers to the charging current value allocated to a single battery cluster.

[0145] In this embodiment of the invention, the charging current of each battery cluster is determined by a correction coefficient for each battery cluster. Specifically, the formula for calculating the charging current is as follows:

[0146] In the formula, Indicates the first i The charging current of each battery cluster; Indicates the first i The reference charging current corresponding to each battery cluster; Indicates the first i Correction coefficients for each battery cluster.

[0147] Step S207: Real-time monitoring of the voltage, current, insulation resistance of the charging circuit and the temperature of each battery cluster.

[0148] It should be noted that the charging circuit refers to the power transmission path that connects the power source, charging equipment, and energy storage battery cluster.

[0149] In this embodiment of the invention, during the charging process, the voltage sensor, current sensor and insulation resistance monitoring module deployed in the charging circuit, combined with the temperature sensor configured in each battery cluster, collect the voltage, current and insulation resistance data of the charging circuit, as well as the operating temperature data of each battery cluster in real time.

[0150] Step S208: Compare the voltage, current, insulation resistance, and temperature of each battery cluster with the corresponding threshold values, and determine abnormal data based on the comparison results.

[0151] It should be noted that abnormal data refers to monitoring parameter data that exceeds the corresponding safety threshold after comparison.

[0152] In this embodiment of the invention, the real-time collected charging circuit voltage, current, insulation resistance, and temperature of each battery cluster are compared with preset voltage safety threshold, current safety threshold, insulation resistance safety threshold, and battery cluster temperature safety threshold one by one. During the comparison process, preset logical judgment rules are executed: if the voltage exceeds the upper or lower limit of the voltage threshold, the current exceeds the upper limit of the current threshold, the insulation resistance is lower than the lower limit of the insulation resistance threshold, or the battery cluster temperature exceeds the upper or lower limit of the temperature threshold, the corresponding parameter is determined to be abnormal data.

[0153] Step S209: Based on the abnormal data, execute the corresponding processing measures.

[0154] It should be noted that the handling measures refer to the pre-set response strategies that match different anomaly types / levels.

[0155] In this embodiment of the invention, abnormal data and their corresponding parameter types and deviation degrees are automatically matched with a preset processing measure library to execute corresponding graded processing actions: if it is a minor abnormality (such as parameters slightly exceeding the threshold), an adjustment command is issued to reduce the charging current, and parameter changes are continuously monitored simultaneously; if it is a moderate abnormality (such as parameters significantly deviating from the threshold), the charging process of the corresponding abnormal monitoring object is suspended, and the system is switched to a backup device (such as a support transformer) to maintain system operation; if it is a severe abnormality (such as a sudden drop in insulation resistance or a sharp rise in temperature), an emergency disconnect command is immediately issued to cut off the power connection of the entire charging circuit, and an alarm is triggered to prompt maintenance personnel to intervene.

[0156] In specific embodiments, the present invention has the following beneficial effects: 1. Dynamic access control for matching "transformer load - charging demand": This breakthrough overcomes the limitation of energy storage units being fixedly connected to specific transformers. By monitoring the load change coefficient and remaining capacity of each transformer in real time, the system dynamically selects the "optimal support transformer" to charge the energy storage compartment. When the target transformer is overloaded, it automatically switches to a less overloaded transformer, achieving full utilization of transformer capacity and global balance of charging load.

[0157] 2. Differentiated charging strategies based on battery health (SOH) and state of charge (SOC): A "packet-level management" and "cluster-level differentiated" charging algorithm was introduced. The system no longer applies the same current to all battery cells, but instead calculates the optimal charging current weight based on the SOH and SOC values ​​of each battery cluster. For battery clusters with high aging levels (low SOH) or high temperatures, the charging current is automatically reduced; conversely, the current is increased, achieving optimal charging tailored to individual needs and greatly improving system lifespan.

[0158] 3. Hybrid energy storage "low-frequency-high-frequency" dynamic decoupled charging technology: In the charging circuit, a bidirectional DC / DC converter is used to dynamically decouple the charging power. Low-frequency components of the charging command are allocated to the lithium battery for energy storage, while high-frequency components (ripple, surges) are allocated to the supercapacitor or flywheel for buffering. This not only improves charging efficiency but also protects the lithium battery by utilizing the "sponge" characteristic of power storage.

[0159] This embodiment also provides an optimal charging control device for an energy storage compartment, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0160] This embodiment provides an optimal charging control device for an energy storage compartment, such as... Figure 3 As shown, it includes: Module 301 is used to build an optimization model based on electricity price data and operating data of multiple devices, with the optimization goal of minimizing charging costs and device lifespan loss. The solution module 302 is used to iteratively solve the optimization model and obtain the optimal charging power reference value for future time periods based on the solution results. The calculation module 303 is used to calculate the load margin of each transformer based on the total capacity, average load and optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand. The judgment module 304 is used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the judgment result. The decomposition module 305 is used to decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and supercapacitor according to the low-frequency components and high-frequency components. The charging module 306 is used to calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and to charge each battery cluster according to the charging current weight corresponding to the priority.

[0161] In some alternative implementations, the construction module 301 includes: The acquisition unit is used to acquire electricity price data for each time of day in the coming day, the remaining power, health status, temperature of all battery clusters inside the energy storage compartment, and the real-time load and maximum capacity of each transformer. The construction unit is used to construct a function of the change of grid electricity price over time based on the electricity price data of each time of day in the future; The lithium battery loss unit is used to determine the lithium battery life loss rate based on the remaining charge, health status and temperature of each battery cluster. The transformer loss unit is used to determine the transformer life loss rate based on the real-time load and maximum capacity of each transformer. A supercapacitor loss unit is used to determine the supercapacitor lifetime loss rate based on the charging and discharging power and voltage fluctuations of the energy storage compartment. The calculation function unit is used to calculate the total life loss function by using the lithium battery life loss rate, transformer life loss rate, supercapacitor life loss rate and corresponding weighting coefficients. Model units are constructed to optimize charging costs and equipment lifespan loss by using the time-varying function of grid electricity price and the total lifespan loss function.

[0162] In some alternative implementations, the computing module 303 includes: Capacity acquisition unit, used to obtain the total capacity and average load of each transformer; The sum calculation unit is used to calculate the sum between the transformer's average load and the optimal charging power reference value; The difference calculation unit is used to calculate the difference between the total capacity of the transformer and the sum value, so as to obtain the load margin of the transformer; The sorting unit is used to sort the load margin of each transformer according to a preset order, determine the transformer with the largest load margin based on the sorting result, and set the transformer with the largest load margin as the support transformer. The first charging unit is used to disconnect the charging connection of the target transformer and switch to the charging connection of the support transformer for continuous charging when the margin of the target transformer is less than a preset margin threshold.

[0163] In some optional implementations, the determination module 304 includes: The judgment unit is used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system. A limiting processing unit is used to limit the charging power of the hybrid energy storage system if the condition is met.

[0164] In some alternative implementations, the decomposition module 305 includes: The decomposition unit is used to decompose the optimal charging power reference value into low-frequency and high-frequency components using a filtering algorithm. The first conversion unit is used to convert low-frequency components into control commands for the lithium battery and charge the lithium battery at constant power according to the control commands. The second conversion unit is used to convert high-frequency components into pulse current commands for the supercapacitor, and to control the supercapacitor to absorb current ripple and transient impacts during the charging process according to the pulse current commands.

[0165] In some alternative implementations, the charging module 306 includes: The normalization unit is used to normalize the remaining power and health status of each battery cluster in the energy storage compartment. The summation unit is used to perform a weighted summation of the remaining charge and health status of each battery cluster after normalization to obtain the priority of each battery cluster. The calculation coefficient unit is used to calculate the correction coefficient of each battery cluster based on the priority of the charging current weight, health status and temperature. The second charging unit is used to determine the charging current of each battery cluster according to the correction coefficient of each battery cluster, and to charge each battery cluster according to the charging current of each battery cluster.

[0166] In some alternative embodiments, the device further includes: The monitoring unit is used to monitor the voltage, current, insulation resistance, and temperature of each battery cluster in the charging circuit in real time. The comparison unit is used to compare voltage, current, insulation resistance and temperature of each battery cluster with the corresponding threshold, and to determine abnormal data based on the comparison results; The execution unit is used to perform corresponding processing measures based on abnormal data.

[0167] The optimal charging control device for the energy storage compartment provided in this embodiment of the invention can execute the optimal charging control method for the energy storage compartment provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0168] This embodiment also provides an optimal charging control system for the energy storage compartment, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0169] This embodiment provides an optimal charging control system for an energy storage compartment. The system includes a control layer and an execution layer connected in sequence. The control layer is used to construct an optimization model based on electricity price data and operating data of multiple devices, with the optimization objective of minimizing charging costs and device lifespan loss. The optimization model is then iteratively solved to obtain the optimal charging power reference value for future periods based on the solution results. The execution layer is used to calculate the load margin of each transformer based on the total capacity, average load, and optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to preset charging demand. The execution layer is also used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system based on the determination result. The execution layer is also used to decompose the optimal charging power reference value into low-frequency and high-frequency components, and control the charging rate of the lithium battery and supercapacitor according to the low-frequency and high-frequency components. The execution layer is also used to calculate the priority of each battery cluster based on the remaining power and health status of each battery cluster in the energy storage compartment, and charge each battery cluster according to the charging current weight corresponding to the priority.

[0170] In this embodiment of the invention, the system specifically includes: Sensing layer: Sensor arrays deployed within the energy storage compartment are used to collect data on individual battery cell voltage, temperature, cluster-level SOC / SOH, transformer load, grid voltage, and electricity price signals.

[0171] Control layer (Energy Management System, EMS): Core processor with built-in charging optimization algorithm model.

[0172] Execution layer: includes high-voltage cascaded PCS (energy storage converter), multiple bidirectional DC / DC converters, and battery management system (BMS).

[0173] Hybrid energy storage system: includes parallel energy-type battery packs (lithium batteries) and power-type units (supercapacitors / flywheels).

[0174] The sensing layer, control layer (energy management system EMS), execution layer, and hybrid energy storage system in this system are mainly used to implement the above embodiments and preferred implementation methods.

[0175] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the optimal charging control method for the energy storage compartment shown in the above embodiments is implemented.

[0176] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An optimal charging control method for an energy storage compartment, characterized in that, The method includes: Based on electricity price data and operational data from multiple devices, an optimization model is constructed with the goal of minimizing charging costs and device lifespan loss. The optimization model is iteratively solved, and the optimal charging power reference value for future time periods is obtained based on the solution results. Based on the total capacity, average load, and optimal charging power reference value of each transformer, the load margin of each transformer is calculated, and the transformer with the largest load margin is switched according to the preset charging demand. Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the determination result; The optimal charging power reference value is decomposed into low-frequency components and high-frequency components, and the charging rate of the lithium battery and supercapacitor is controlled according to the low-frequency components and the high-frequency components. The priority of each battery cluster is calculated based on its remaining power and health status in the energy storage compartment, and each battery cluster is charged according to the charging current weight corresponding to the priority.

2. The method according to claim 1, characterized in that, Based on electricity price data and operational data from multiple devices, an optimization model is constructed with the goal of minimizing charging costs and device lifespan degradation. This model includes: Obtain electricity price data for each hour of the next day, the remaining power, health status, temperature of all battery clusters inside the energy storage compartment, and the real-time load and maximum capacity of each transformer; Based on the electricity price data for each time of day in the future, a function for the change of grid electricity price over time is constructed. The lithium battery life loss rate is determined based on the remaining charge, health status, and temperature of each battery cluster. The transformer life loss rate is determined based on the real-time load and maximum capacity of each transformer. The lifespan loss rate of the supercapacitor is determined based on the charging and discharging power and voltage fluctuation of the energy storage chamber. The total lifespan loss function is calculated using the lithium battery lifespan loss rate, the transformer lifespan loss rate, the supercapacitor lifespan loss rate, and the corresponding weighting coefficients. With the optimization objectives of minimizing charging costs and equipment lifespan loss, an optimization model is constructed using the time-varying function of the grid electricity price and the total lifespan loss function.

3. The method according to claim 1, characterized in that, The step of calculating the load margin of each transformer based on its total capacity, average load, and the optimal charging power reference value, and switching to the transformer with the largest load margin according to preset charging demand, includes: Obtain the total capacity and average load of each transformer; Calculate the sum between the average load of the transformer and the optimal charging power reference value; Calculate the difference between the total capacity of the transformer and the sum value to obtain the load margin of the transformer; The load margins of each transformer are sorted according to a preset order. Based on the sorting results, the transformer with the largest load margin is determined and set as the support transformer. When the margin of the target transformer is less than the preset margin threshold, the charging connection of the target transformer is disconnected and the charging connection of the support transformer is switched to continue charging.

4. The method according to claim 1, characterized in that, The step of determining whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjusting the charging power of the hybrid energy storage system according to the determination result, includes: Determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system; If so, the charging power of the hybrid energy storage system will be limited.

5. The method according to claim 1, characterized in that, The step of decomposing the optimal charging power reference value into low-frequency and high-frequency components, and controlling the charging rate of the lithium battery and supercapacitor according to the low-frequency and high-frequency components, includes: The optimal charging power reference value is decomposed into low-frequency and high-frequency components using a filtering algorithm; The low-frequency component is converted into a control command for the lithium battery, and the lithium battery is charged at a constant power according to the control command. The high-frequency components are converted into pulse current commands for the supercapacitor, and the supercapacitor is controlled to absorb current ripple and transient impacts during the charging process according to the pulse current commands.

6. The method according to claim 1, characterized in that, The step of calculating the priority of each battery cluster based on its remaining power and health status in the energy storage compartment, and charging each battery cluster according to the charging current weight corresponding to the priority, includes: The remaining power and health status of each battery cluster in the energy storage compartment are normalized. The priority of each battery cluster is obtained by weighted summation of the remaining power and health status of each battery cluster after normalization. The correction coefficient for each battery cluster is calculated based on the priority of the charging current weight, health status, and temperature. The charging current of each battery cluster is determined according to the correction coefficient of each battery cluster, and each battery cluster is charged according to the charging current of each battery cluster.

7. The method according to claim 1, characterized in that, The method further includes: Real-time monitoring of the voltage, current, insulation resistance, and temperature of each battery cluster in the charging circuit; The voltage, the current, the insulation resistance, and the temperature of each battery cluster are compared with their respective thresholds, and abnormal data are determined based on the comparison results. Based on the abnormal data, corresponding processing measures will be implemented.

8. An optimal charging control device for an energy storage compartment, characterized in that, The device includes: The module is used to build an optimization model based on electricity price data and operating data of multiple devices, with the optimization goal of minimizing charging costs and device lifespan loss; The solution module is used to iteratively solve the optimization model and obtain the optimal charging power reference value for future time periods based on the solution results. The calculation module is used to calculate the load margin of each transformer based on the total capacity, average load and the optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand. The judgment module is used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the judgment result. The decomposition module is used to decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and the supercapacitor according to the low-frequency components and the high-frequency components. The charging module is used to calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and to charge each battery cluster according to the charging current weight corresponding to the priority.

9. An optimal charging control system for an energy storage compartment, characterized in that, The system includes a control layer and an execution layer connected in sequence; The control layer is used to construct an optimization model based on electricity price data and operating data of multiple devices, with the optimization objective of minimizing charging costs and device lifespan loss; The optimization model is then iteratively solved, and the optimal charging power reference value for future time periods is obtained based on the solution results. The execution layer is used to calculate the load margin of each transformer based on the total capacity, average load and the optimal charging power reference value of each transformer, and switch to the transformer with the largest load margin according to the preset charging demand. The execution layer is also used to determine whether the optimal charging power reference value is greater than the maximum charging power threshold of the hybrid energy storage system, and adjust the charging power of the hybrid energy storage system according to the determination result. The execution layer is also used to decompose the optimal charging power reference value into low-frequency components and high-frequency components, and control the charging rate of the lithium battery and the supercapacitor according to the low-frequency components and the high-frequency components. The execution layer is also used to calculate the priority of each battery cluster according to the remaining power and health status of each battery cluster in the energy storage compartment, and to charge each battery cluster according to the charging current weight corresponding to the priority.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the optimal charging control method for the energy storage compartment as described in any one of claims 1 to 7.

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